Computational Modelling of Cardiorespiratory Coupling During Exercise and Its Application to Athletic Performance Prediction in Elite Nigerian Runners

Cardiorespiratory coupling during exercise reflects the degree of physiological integration between cardiac and respiratory rhythms, and its mathematical characterisation may provide novel indices of athletic performance capacity. This study developed a computational model of cardiorespiratory coupling during graded exercise and validated it against direct performance metrics in elite Nigerian middle-distance runners, making a methodological contribution to sports physiology in the African context. A prospective experimental design enrolled 45 elite national-level runners training at the National Institute for Sports, Lagos, South West Nigeria. Participants underwent maximal cardiopulmonary exercise testing with simultaneous continuous electrocardiography and respiratory inductance plethysmography. Cardiorespiratory coupling was quantified using cross-spectral coherence, phase synchronisation, and multiscale entropy methods applied to heart rate and respiratory cycle series. These coupling indices were correlated with VO2max, lactate threshold, and competitive race times. A predictive linear model was constructed and validated by leave-one-out cross-validation. Results showed that cardiorespiratory phase synchronisation at moderate exercise intensity was the strongest predictor of VO2max and 1500-metre race performance. High coupling efficiency correlated with superior lactate threshold values. The computational model predicted VO2max with a root mean square error of 1.8 millilitres per kilogram per minute. This study presents an original computational physiological framework for performance prediction, previously unavailable for Nigerian athletic populations. Applications in talent identification and training optimisation are proposed. Keywords: cardiorespiratory coupling, computational model, athletic performance, VO2max, Nigerian runners.